orbit-watermark 1.1.1

High-robustness neural audio watermarking using SilentCipher (Sony AI) & Spread Spectrum fallback

@ohnrshyp/watermark

Robust neural audio watermarking using SilentCipher.

This module implements digital steganographic audio watermarking. Based on Sony AI's SilentCipher, it encodes imperceptible digital payloads directly within audio waveforms. These neural watermarks are highly robust and survive lossy compression (MP3, AAC, Opus, M4A), frequency downsampling, time-stretching, and format conversions with over 99% extraction accuracy.

---

🚀 Key Features

• 🧠 Neural Steganography (SilentCipher): Embeds a 40-bit payload into an audio signal imperceptibly (high Signal-to-Distortion Ratio / SDR).
• 🛡️ Compression & Transcoding Survival: Payloads remain extractable after standard music distribution pipelines and MP3 conversion.
• ⚙️ Dual Venv Orchestration: Automatically isolates its run environment using a dedicated virtual environment with torch<=2.0.0 to prevent system conflicts.
• 🔍 Prefix Match Queries: Translates a 32-byte registration hash into a 5-byte message and verifies matches cryptographically.

---

🔬 Architectural & Mathematical Design

The watermarking engine bridges a Node.js child process wrapper and a high-performance Python script executing the SilentCipher deep model.

1. Neural Audio Steganography

SilentCipher utilizes a deep encoder-decoder network trained with adversarial and psychoacoustic losses.

• The Encoder generates an imperceptible perturbation waveform: $$y_{\text{watermarked}} = y_{\text{input}} + \epsilon(y_{\text{input}}, M)$$ where $M$ is the 40-bit message. The encoder is optimized to keep the Signal-to-Distortion Ratio (SDR) high ($\ge 20$ dB), satisfying strict human hearing thresholds: $$\text{SDR} = 10 \log_{10} \left( \frac{\sum y_{\text{input}}^2}{\sum (y_{\text{watermarked}} - y_{\text{input}})^2} \right)$$
• The Decoder reconstructs the 40-bit message from $y_{\text{watermarked}}$ even if the signal has undergone channel distortions (clipping, compression, noise).

2. Message Encoding & Hash Prefixing

SilentCipher has a native capacity of 40 bits, represented as 5 bytes (unsigned 8-bit integers: 0-255 each).

• To encode a 32-byte transaction/registration hash, ORBIT truncates it to its first 5 bytes: $$\text{Message} = \text{Hash}[0 \dots 4]$$
• On extraction, the extracted 5-byte buffer is compared with the prefix of the expected 32-byte registration hash: $$\text{isMatch} = (\text{Extracted Hash}[0 \dots 4] \equiv \text{Expected Hash}[0 \dots 4])$$ This prefix matching mechanism allows fast lookups in the ledger while maintaining a collision probability of only $1$ in $1.09$ trillion.

---

📦 Installation

Node.js (NPM Package)

npm install @ohnrshyp/watermark

Python (PyPI Package)

pip install orbit-watermark

Python Isolation Setup

SilentCipher requires a specific old version of PyTorch. To avoid conflicts with your main machine learning environment, create an isolated virtual environment (.venv-watermark/):

# Create venv
python -m venv .venv-watermark
source .venv-watermark/bin/activate

# Install dependencies
pip install torch==2.0.0 silentcipher librosa soundfile numpy

Export the python path environment variable to point to this venv:

export ORBIT_SILENTCIPHER_PYTHON=/absolute/path/to/your/ORBIT/.venv-watermark/bin/python3

---

🛠️ API Reference

embed(input, payloadHash, [options])

Embeds a 5-byte prefix of a 32-byte registration hash into an audio source.

• Parameters:
  • input (Buffer | string): Raw binary buffer or absolute path to the target audio.
  • payloadHash (Buffer): 32-byte registration hash buffer.
  • options (Object, optional):
    • outputPath (string): Path to save the watermarked file. If not provided, it writes to a temporary WAV file.
    • verbose (boolean): Enable diagnostic logging. Default is false.
• Returns: Promise<Object>:

  {
    "success": true,
    "outputPath": "/tmp/orbit-sc-output.wav",
    "sdr": 28.51,
    "message": [161, 23, 192, 45, 99],
    "duration": 180.5,
    "processingTimeMs": 2400,
    "method": "silentcipher"
  }
  

extract(input, [options])

Extracts the embedded watermark from an audio file.

• Parameters:
  • input (Buffer | string): Raw binary buffer or absolute path to the target audio.
  • options (Object, optional):
    • phaseShiftDecoding (boolean): Enable robust phase-shift decoding. Improves detection accuracy on cropped or time-stretched files. Default is true.
    • verbose (boolean): Enable diagnostics. Default is false.
• Returns: Promise<Object>:

  {
    "success": true,
    "detected": true,
    "message": [161, 23, 192, 45, 99],
    "payloadHash": <Buffer a1 17 c0 2d 63>,
    "confidence": 0.9412,
    "duration": 180.5,
    "processingTimeMs": 1850,
    "method": "silentcipher"
  }
  

hashToMessage(payloadHash)

Converts a 32-byte Buffer to a 5-element array of unsigned integers.

• Returns: Array<number>

messageToHash(message)

Converts a 5-element array of unsigned integers back to a 5-byte Buffer.

• Returns: Buffer

hashMatches(extractedHash, expectedHash)

Helper that returns true if extractedHash matches the first 5 bytes of expectedHash.

• Returns: boolean

checkPythonEnvironment()

Verifies that the Python binary and dependencies are available and operational.

• Returns: Promise<Object>

---

💻 Code Examples

Embedding and Verifying a Watermark

const watermark = require('@ohnrshyp/watermark');
const fs = require('fs');

async function run() {
  const originalAudio = fs.readFileSync('input-track.wav');
  
  // A unique 32-byte registration hash from your ledger
  const registrationHash = Buffer.from('a117c02d63ef5a8b9c2d1e2f3a4b5c6d7e8f9a0b1c2d3e4f5a6b7c8d9e0f1a2b', 'hex');

  try {
    // 1. Embed the watermark prefix
    const embedResult = await watermark.embed(originalAudio, registrationHash, {
      outputPath: 'watermarked-track.wav',
      verbose: true
    });

    console.log(`Watermark embedded successfully. SDR: ${embedResult.sdr} dB`);

    // 2. Read watermarked file back (e.g. simulating a download)
    const watermarkedAudio = fs.readFileSync('watermarked-track.wav');

    // 3. Extract the watermark
    const extractResult = await watermark.extract(watermarkedAudio, {
      verbose: true
    });

    if (extractResult.detected) {
      console.log('Watermark detected! Payload hash prefix:', extractResult.payloadHash.toString('hex'));
      
      // 4. Verify match against expected registration hash
      const isMatch = watermark.hashMatches(extractResult.payloadHash, registrationHash);
      console.log(`Payload match verification: ${isMatch}`);
    } else {
      console.log('No watermark detected in file.');
    }

  } catch (error) {
    console.error('Watermarking pipeline failed:', error.message);
  }
}

run();

---

📄 License

Licensed under the Apache License, Version 2.0 (the "License"). See LICENSE in the project root for details.